D-3938
ECONOMICS, TECHNOLOGY, AND THE ENVIRONMENT
by
Professor Jay W. Forrester
Germeshausen Professor
WP-1983-88
November 23, 1987
D-3938
1
ECONOMICS, TECHNOLOGY, AND THE ENVIRONMENT
by
Professor Jay W. Forrester
Germeshausen Professor
System Dynamics Group
Sloan School of Management
Massachusetts Institute of Technology
Cambridge, Massachusetts 02139, USA
at the
Agricoltura 2000 Award Ceremony
for advancement of environmental studies
Rome, Italy
November 23, 1987
D-3938
2
1.
THE NATURE OF SOCIAL SYSTEMS
4
2.
UNDERSTANDING
5
3.
4.
SOCIAL SYSTEMS
2.1.
System Dynamics
5
2.2.
Mental Models
~6
2.3.
Combining Mental and ComputerModels
6
2.4.
Characteristicsof Social Systems
7
MODES OF ECONOMIC BEHAVIOR
8
3.1.
Life Cycle of Economic Development
8
3.2.
Economic Long Wave
9
AGRICULTURE
12
4.1.
Economic Long Wave Affects Agriculture
12
4.2.
Life Cycle of Growth Affects Agriculture
13
D-3938
3
ECONOMICS, TECHNOLOGY, AND THE ENVIRONMENT
Professor Jay W. Forrester
Germeshausen Professor
System Dynamics Group
Sloan School of Management
Massachusetts Institute of Technology
Cambridge, Massachusetts 02139, USA
Rome, Italy
November 23, 1987
Will recent decline of stock markets around the world cause
another great depression? No, not by themselves. But unstable stock
markets are a symptom of deeper forces. Unfavorable pressures have
been growing for many decades. Those forces can lead into a major
economic downturn.
The world is now influenced by changes from three directions.
First, economic forces are causing greater unemployment, increasing
trade protectionism, idling manufacturing capacity, and raising
defaults on debt. Second, new technologies are demanding different
worker skills, redirecting trade routes, and altering production
methods. Third, environmental deterioration is threatening health,
killing forests, poisoning water tables, and modifying climate.
All these forces affect the future of agriculture. Agriculture
operates at the intersection of economics, technology, and the
environment. Throughout history, changing economic forces of supply
and price have dominated the well-being of people in agriculture. For
the last hundred years, technology has offered hope of more plentiful
and less expensive agricultural products. During the last several
decades, pesticides and herbicides have increased agricultural yields.
But now, environmental damage from those technological advances is
forcing reconsideration of past agricultural practices.
Such social forces are only partially perceived. They are poorly
understood. By contrast, the last century has seen great advances in
understanding physical science and its application to technology.
During that same time there have been no corresponding
improvements in understanding society, economics, or the
relationship of people to the natural world within which we live. As
D-3938
4
has been true for centuries, we still have imbalances in trade,
difficulties with exchange rates, inflation, and wars.
In reaction to unfavorable social pressures, governments change
regulations, increase expenditures, and alter tax laws. In doing so,
governments are running experiments on actual full-scale economies.
But the complexity of social systems often obscures the relationships
between policies and their consequences. Because cause and effect
are not correctly perceived, social experiments fail far too often.
By contrast, methods for understanding systems in modern
technology are well developed. If a corporate executive were planning
a new kind of chemical plant, he would not start by building a full scale
installation. He would not use the final system for experimenting with
the separate processes and their interactions. Instead, he would first
make limited experiments, using small pilot plants and computer
simulation models until the processes were well understood.
1. THE NATURE OF SOCIAL SYSTEMS
Why has our knowledge of social systems progressed so little
since ancient times? The reason is not because social systems differ
in kind from physical systems that can be well understood. The
answer lies in the greater complexity of social systems along with the
inability of the human mind to comprehend the behavior of such
systems.
Although most people believe otherwise, social systems belong
to the same class as physical systems. The difficulty in understanding
social systems does not arise because social systems are fundamentally
different from physical systems. Social systems, like physical systems,
are multiple-loop, information-feedback structures. They belong to
the class that is mathematically described as high-order, nonlinear,
differential equations. In science and engineering, no one attempts to
solve such systems by intuition and debate. In technology, computer
simulation models are used to reveal the behavior that would be
created by a particular system structure.
But managers, politicians, and even most people in the social
sciences are not aware of the extreme difficulty presented by dynamic
behavior in social systems. They do not realize that such systems lie
beyond the power of the traditional processes of thought and
discussion. They do not know that methods now exist for reaching a
far better understanding of such systems. Leaders of our political and
business institutions still try to manage social and economic behavior
by using the inadequate methods inherited from the past. The
traditional processes based on historical precedent are not sufficient
D-3938
5
for understanding the interrelationships of economics, technology,
society, and the environment.
2.
UNDERSTANDING SOCIAL SYSTEMS
Methods for achieving more desirable human systems are now
available. Powerful computer simulation methods, originally developed
for technological systems, have advanced to a point where they can
deal with the far greater complexity of social and economic systems.
Those responsible for managing countries have not in the past
been able to test policy changes in the laboratory before trying them
full-scale on entire populations. Now, however, computer simulation
models are becoming laboratories for evaluating economic policy
alternatives quickly and at low cost before new policies are committed
to broad social implementation.
Such simulation models can combine the advantages of the
human mind with the advantages of modern digital computers, while
avoiding the weaknesses of both. But relatively few resources are now
being directed to achieving a better understanding of how to cope
with deficits, trade imbalances, debts, inflation, unemployment, and
environmental degradation.
2.1. System Dynamics
During the last thirty years, a professional field known as
"system dynamics" has been established to develop computer
simulation models based on the institutional knowledge of managers
and political leaders. World-wide participants in the field have formed
the System Dynamics Society. The 1986 international meeting of the
Society was held in Seville, Spain, and this year in Shanghai, China.
Next year the conference will be in California, and the year after in
Germany. A regional chapter of the System Dynamics Society is now
being formed in the Latin countries including Italy, France, Spain, and
Portugal.
In a system dynamics study, one builds a computer model that
acts out the behavior of the real system that is being represented.
Such a computer model is not based on difficult mathematics. It is a
role-playing model that demonstrates how the parts of a system
influence one another. The computer model is then used to
understand how the components of a system interact to produce the
observed behavior. When the behavior is not as desired, the model is
used as a laboratory in which policies can be changed to determine
how the behavior of the system could be altered.
11
D-3938
6
2.2. Mental Models
The use of models may at first sound unfamiliar. But everyone
uses models. All decisions are made on the basis of models. A person
does not have an actual family, or city, or business, or country in his
head. Instead, all decisions are made on the basis of assumptions,
perceptions, and recollections. Those mental images are models. We
use mental models to make decisions and to anticipate the future
results of our actions.
Mental models have both great strengths and severe weaknesses.
The mental models are excellent sources of reliable information about
the structure of social systems and the policies being followed in
making decisions. But mental models often give the wrong answers
about how those structures and policies interact to produce behavior.
On the other hand, a computer model can show with certainty
the dynamic consequences of the structural and policy assumptions
that are built into the model.
2.3. Combining Mental and Computer Models
The promise for the future lies in combining the advantages of
mental models with the advantages of computer models. In doing so,
one uses the insights that people already possess in their heads about
how decisions are made, what information is available at each
decision-making point, how various kinds of crises affect decisions at
each place in a social system, and the local goals that motivate
decisions. From such knowledge, one then constructs a replica of the
actual system in the form of a computer simulation model. The
behavior of such a model is usually different from what people thought
was implied by the system structure with which they are familiar.
We often see the discrepancy between actual behavior and that
which is expected from the policies being followed. For example, one
can go into a company that has a severe and widely known difficulty,
such as falling market share, or especially low profitability, or unusual
fluctuation of employment. Interviews reveal descriptions of policies
followed within the company. Such policies are often justified on the
basis that they are aimed at solving the difficulty. The asserted
policies are then used to construct a system dynamics model. To the
surprise of most people, the model manifests the serious symptoms
arising within the actual company. In other words, the corporate
difficulty is implicit in the policies that people know they are
following. Such a situation is treacherous. People believe that their
policies lead toward a solution. But, in the complexity of the situation
they do not realize that their policies are causing the problem. So, as
7
D-3938
matters get worse, increasing troubles are interpreted as reasons to
take more of the very actions that are causing the difficulties.
Within a computer simulation model, one can locate the policies
that are most responsible for undesirable behavior. Alternative
policies can be tested to learn how to improve behavior.
2.4. Characteristicsof Social Systems
More important than finding unexpected behavior in a specific
system is the discovery of general characteristics that are applicable to
a broad class of systems, or even to nearly all systems. Kinds of
behavior can be identified that are usually valid and give an improved
basis for thinking about systems.
Such generalizing usually establishes scientific explanations for
the timeless lessons brought to us through history, myths, fables, and
religions. The lessons from such traditional sources contain powerful
threads of truth that are being ignored in modern thinking that is
dominated by short-run considerations. 1 Some examples will
illustrate.
First, a long-term versus short-term trade off occurs in most
decisions. A policy that produces favorable results in the short run
will usually produce unfavorable results in the long run, and vice versa.
The inherent conflict between immediate and ultimate consequences
is not given proper weight in management and political decisions. On
the other hand, the recognition of the inherent conflict between the
present and the future goes back at least as far as the ancient Greeks.
Aesop's fable of the grasshopper and the ant contrasts the short-term
advantage of playing in the summer with the long-term penalty of
freezing in the winter.
Another general characteristic of systems lies in the high
resistance to most policy changes. Perhaps as many as 98 percent of
the policies in a system have little effect on behavior. Systems are an
assembly of feedback loops that can compensate for changes in most
policies. Yet much of the debate in business and government is
directed at those low-leverage policies. Much time and effort is
expended in arguing over policies that have almost no effect. A skillful
system dynamics study can distinguish between the low-leverage
policies and the very few influential policies that have the potential for
improving conditions.
1 Several general characteristics of systems were identified in UrbanDynamics (Forrester
1969, 107-114).
D-3938,
8
To help build a public understanding of social systems, we should seek
those general insights that can connect current problems with themes
from the traditional truths inherited from the past.
3. MODES OF ECONOMIC BEHAVIOR
National economies exhibit several different kinds, or modes, of
economic behavior. On the shortest time scale, ordinary business
cycles are fluctuations in economic activity with peaks from three to
ten years apart. On an extended time scale, the economic long wave,
or Kondratieff cycle, is a large rise and fall in world economies with
peaks 45 to 60 years apart. On a still longer time scale, the life cycle
of economic development describes growth of population and
industrialization until the environment is overloaded and resists
further growth. All of these forces are now combining to create the
economic stresses that dominate newspaper headlines and political
debate.
3.1. Life Cycle of Economic Development
The life cycle of economic development can extend over two
hundred years or more. It represents the time during which
population and technology grow until they can no longer be supported
by the capacity of nature to supply food, water, resources, and
pollution-dissipation capacity. Social and technological changes over
such a long time scale are hard to see and to understand. Computer
simulation allows a time compression so that our assumptions about
the future can be examined. One needs to bring space, time,
technology, and social change into a common framework for
examination and discussion. A common language is needed for dealing
with change.
System dynamics can provide a powerful language for
communication. Assumptions for a model are explicitly stated so that
they can be debated and improved. Behavior of a model can be
compared with real-life observed behavior. A model permits complete
internal consistency between assumptions, behavior, and the
consequences of recommended policy changes.
The communication process based on a system dynamics model
is illustrated by the Limits to Growth book 2 . Limits to Growth
describes a model of the life cycle of economic development. It shows
how population, industrialization, resources, crowding, and pollution
2 Meadows, Donella H., et al, 1972, also the predecessor book, World Dynamics,
Forrester, 1971.
D-3938
9
lead to counterpressures from a stressed natural environment. The
book resulted from a joint project between the Club of Rome, founded
by the late Aurelio Peccei who lived here in Italy, and the System
Dynamics Group at the Massachusetts Institute of Technology.
Limits to Growth produced widespread public debate about
economic development. Although some critics claim the book has
been discredited, a careful reading shows that it demonstrates forces
that now appear in newspaper headlines describing social turmoil
from crowding, hunger in many countries, and pollution of the
environment.
The book has been translated into about 30 languages and has
sold some 3 million copies. Following publication in 1972, it was
discussed in conferences on university campuses, in the developed
and developing countries, and at meetings of corporate executives.
The book provoked spirited debate in newspaper editorials and on
television programs.
Why would a book based on a computer simulation model
receive such extensive public attention? There seem to be several
reasons. The book addressed concerns of the public about the
population explosion, crowding, and pollution. It helped meet the
human desire to see the future more clearly. More than that, it put
the population and industrialization picture together. It showed how
economic growth related to environmental concerns. Limits to
Growth combined descriptive text with organizational diagrams and
computer simulations of behavior over past and future time. It
provided a clarity that is missing from books that attempt to treat
complexity using only written language.
3.2. Economic Long Wave
The world economy is unbalanced. Economic instability is
increasing. Huge Latin American debts are not likely to be repaid.
Speculation in physical assets has led to large discrepancies among
many prices and wages. Trade imbalances are threatening economic
stability. Many government deficits are out of control. Currency
fluctuations are disrupting trade. Inflation has been interfering with
commercial activity.
Are such forces only coincidences? Or, are they connected
below the surface in a powerful process of economic change? I believe
the current economic imbalances are connected by a mode of behavior
called the economic long wave, which is popularly known as the
Kondratieff cycle.
11
D-3938
10
The economic long wave has been controversial, both as to its
cause and even to its existence. Those who believe that the economic
long wave exists see it as a great rise and fall of economic activity with
peaks and valleys some 45 to 60 years apart. It is considered the
cause of the great depressions of the 1830s, 1890s, and 1930s. For a
century, the nature of the economic long wave remained unclear
because in the past there has been no theory for how the long wave
could be generated. Instead, major depressions have often been
attributed to accidents or to governmental mismanagement. For
example, in the United States, the Great Depression of the 1930s has
often been blamed on nothing more than mistaken policies of the
monetary authority.
Traditional theory has been unable to explain large changes in
economic behavior. Most economic theory has been based on
equilibrium, or steady-state, concepts that leave little room for even
imagining an economy that can persist in major long-term fluctuations
between booms and depressions.
However, within the last few years, a comprehensive theory of
the economic long wave has come into existence. It is in the form of
the System Dynamics National Model developed in the System
Dynamics Group of the Sloan School of Management at the
Massachusetts Institute of Technology. The National Model is a
computer simulation model based on the policies followed in banks,
industries, markets, and government. The model is self-contained.
Unlike the more common econometric models, the National Model
operates without external driving inputs for controlling its behavior.
In the National Model we find that ordinary and well-known
policies of business, banking, and government can interact to produce
all the important kinds of behavior observed in an economy. The
National Model generates the well-known short-term business cycle
with peaks some 3 to 10 years apart. It exhibits stagflation
(simultaneously rising unemployment and inflation), as occurred in the
1970s, but which had previously been considered by many to be
impossible. The National Model also creates an economic long wave
with a major rise and fall of economic activity having peaks some 50
years apart.
The System Dynamics National Model provides a theory of how
the economic long wave occurs. A computer simulation model is a
theory of the behavior that it creates. The structure of a model and
the decision-making policies within it cause the resulting behavior.
One can examine a model to see why the behavior happens. The
System Dynamics National Model shows how production, investment,
saving, construction, and credit can interact to produce great waves of
excessive economic expansion and contraction that extend over
several decades.
D-3938
11
The National Model provides a new perspective from which to
interpret economic behavior. In the early part of a long-wave
expansion, as in the 1950s and 1960s, money is borrowed to build
factories. After a peak, as at the present time, depreciation cash flows
and new borrowing are used for speculation in land, for corporate
acquisitions, and for bidding up prices in the equities markets beyond
the underlying business realities.
Conditions during the 1980s have closely paralleled those of the
1920s. There were many corporate mergers in the late 1920s as
there have been recently. The price of agricultural land in the United
States reached a peak in 1920 and again in 1980. The variation of
land prices before and after the peaks are surprisingly similar. Land
prices in American agriculture had fallen to half their 1920 peak by
the late 1920s and are now down to about half their 1980 peak. Land
prices will probably fall further in the 1990s just as they did in the
1930s.
The central driving force of the long wave is the over-building
and under-building of physical capital investment, such as factories,
machinery, offices, apartments, and homes. With this fluctuation of
physical investment go other reinforcing changes in an economy.
During some three decades of expansion, construction of physical
facilities increases employment and personal income. The growing
income supports more purchasing and creates the need for still more
production facilities. As an economy moves toward a peak, the need
for more physical investment diminishes. But governments make
credit more freely available and change taxes to sustain the boom in
construction. Such actions by governments result in still more excess
physical investment and contribute to a more severe decline later.
The excesses from the long-wave expansion are now evident as
over-capacity in many industries and as falling agricultural prices. But
many imbalances remain. The larger economic corrections still lie
ahead.
After examining a broad range of evidence, one can conclude
that the peak of the recent long-wave expansion occurred at about
1980 and that the best estimate for the low point in the downturn is
the mid-1990s. This means another decade of economic difficulty as
the great imbalances in world economies are readjusted.
The economic long wave is a world-wide phenomenon. Trade
and money flows lock the world into about the same timing of longwave rise and fall. Excess production exists on an international basis.
Every country is trying to solve its domestic economic weakness by
exporting more than it imports, but that is not possible. The solution
III
D-3938
12
to economic difficulties must come from inside each country.
Individual internal economic balances must be reestablished.
Long-wave peaks and downturns are times of great international
danger. Wars have tended to occur at the peaks and valleys. World
War I occurred at a peak of the long wave and World War II during a
valley. In a downturn, as internal economic difficulties become more
acute, there is a tendency for governments to divert attention of
citizens away from domestic economic troubles by focusing public
attention on an exaggerated external threat. However, the greatest
threat to future well-being of most countries now comes not from the
outside but from the inside in worsening domestic social and
economic conditions.
4. AGRICULTURE
The economic long wave and the life cycle of economic
development both affect agriculture. We are entering a downturn in
the long wave, and are moving into the end of the life cycle of growth.
4.1. Economic Long Wave Affects Agriculture
At the peak of a long wave, production in most industries has
risen above market demand. In the downturn, falling demand puts
downward pressure on prices. In the production of factory goods,
declining demand is closely matched by reduced production. But
agriculture responds differently, at least over a short period of a few
years. The fixed costs in agriculture are high. People on the land are
less likely to leave than are factory workers to be dismissed. As a
result, agriculture tries to maintain income by increasing production
at a time of falling prices.
For the individual farmer, greater production at a lower price
seems a way to maintain income. However, when production rises at a
time of falling demand, downward pressures on prices increase.
During a long wave downturn, such as we are now entering, prices of
agricultural crops will tend to fall further than the prices of
manufactured goods. In other words, the prices of what farmers sell
will fall relative to the prices of what farmers buy. This can throw a
greater economic hardship on the agricultural sector than on other
business sectors. I see the next ten years as a particularly unfavorable
economic time for agriculture.
13
D-3938
4.2. Life Cycle of Growth Affects Agriculture
The life cycle of growth changes the traditional relationships of
agriculture to other parts of an economy. Population growth and
increasing industry encroach on agriculture. Housing and industries
occupy more and more of the best land that should have been
preserved for food production. Demands for water by people and
industry restrict the irrigation water available for fanning. Acid rain
from burning coal and oil is detrimental to vegetation. Sickness
caused by agricultural chemicals will lead to restrictions on chemical
usage. As a result of these pressures, agriculture will become less
efficient. More inputs of labor and energy will be required for growing
food.
I believe a time will come when movement of people from
agriculture to industry will begin to reverse. This may happen as soon
as during the next 30 years. As population grows and requires more
food, and as food-growing capacity declines, a sharp reversal can occur
from having excess food to having a shortage of food. This has already
occurred in some African countries and elsewhere. The industrial
countries are not immune to a similar rather sudden reduction in
adequacy of food.
Behind the long sweep of the life cycle of economic growth lies the
pressure of rising population. It is population growth, with the
accompanying pollution and demands for resources and space, that
create the clash between man and nature. The standard of living is
maximum half way up the growth curve. Beyond that half-way point,
and we are about there, environmental resistance increases rapidly.
More and more effort must be expended to cope with the unfavorable
consequences of an overloaded environment.
Economic and social forces surrounding agriculture will
increase. Short-term political responses to those forces can be
detrimental. Recent developments in computer modeling can help to
anticipate and understand changes. By testing policies in simulation
models, it is possible to test the consequences of various laws and
regulations. The agricultural community should exert leadership in
examining what present laws and practices imply for the future of
agriculture as agriculture interacts with economics, technology and
the environment.
REFERENCES
Forrester,JayW. 1969. UrbanDynamics. Cambridge,MA.: MITPress.
1971. WorldDynamics. Cambridge,MA.: MITPress.
1977. Growth Cycles. De Economist 125(4): 525-543. Leiden:
Kroese.
III
14
D-3938
1979. An Alternative Approach to Economic Policy: Macrobehavior
from Microstructure. Economic Issues of the Eighties. Nake M. Kamrany
and Richard H. Day,editors: 80-108. Baltimore, MD.: Johns Hopkins
University Press.
1980. Information Sources for Modeling the National Economy. J. of
the American StatisticalAssociation 75(371): 555-574.
Forrester, Nathan B. 1982. A Dynamic Synthesis of Basic Macroeconomic Theory:
Implicationsfor StabilizationPolicy Analysis. Ph.D. Dissertation.
Cambridge, MA.: Sloan School of Management, Massachusetts Institute of
Technology.
Freeman, Christopher 1983. Long Waves in the World Economy, London and Boston:
Butterworths.
Graham, Alan K. 1980. Parameter Estimation in System Dynamics Modeling. Elements
of the System Dynamics Method, Jorgen Randers, editor: 143-161. Also in
System Dynamics, Vol. 14 in Studies in the Management Sciences: 125-142,
New York: North Holland.
Kondratieff, Nikolai 1984. The Long Wave Cycle, a recent translation of classic papers
from the 1920s, New York: Richardson and Snyder, available through E. P.
Dutton
Low, Gilbert W. 1980. The Multiplier-Accelerator Model of Business Cycles Interpreted
from a System Dynamics Perspective. Elements of the System Dynamics
Method, Jorgen Randers, editor: 76-94. Cambridge, MA.: M.I.T. Press.
Mass, Nathaniel J. 1975. Economic Cycles: An Analysis of Underlying Causes.
Cambridge, MA.: MIT Press.
Meadows, Donella H., et al 1972. The Limits to Growth. New York: Universe Books.
Richmond, Barry M. 1985. STELLA: Software for Bringing System Dynamics to the
Other 98%. Proceedingsof the 1985 InternationalConference of the System
Dynamics Society. Cambridge, MA.: System Dynamics Society.
Roberts, Nancy 1978. Teaching Dynamic Feedback Systems Thinking: an Elementary
View. ManagementScience, 24(8): 836-843
Senge, Peter M. 1985. System Dynamics, Mental Models, and the Development of
Management Intuition. Proceedingsof the 1985 InternationalConference of
the System Dynamics Society. Cambridge, MA.: System Dynamics Society.
Sterman, John D. 1985. A Behavioral Model of the Economic Long Wave. J. of
Economic Behavior and Organization,6(2): 17-53.
1986. The Economic Long Wave: Theory and Evidence. System
Dynamics Review. 2(2): 87-125.
D-3938
Sterman, John D. and Dennis Meadows. 1985. STRATEGEM-2: A Microcomputer
Simulation Game of the Kondratiev Cycle. Simulation and Games 16(2):
174-202.
van Duijn, J. J. 1983. The Long Wave in Economic Life. London: George Allen &
Unwin.
Vescuso, Peter, 1985. Using STELLA to Create Learning Laboratories: An Example
from Physics. Proceedingsof the 1985 InternationalConference of the
System Dynamics Society. Cambridge, MA.: System Dynamics Society.
15